Skip to main content

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 210))

  • 905 Accesses

Abstract

IoT systems are one of the most important areas of developing technology. IoT application solutions are becoming widespread and their usage areas are expanding. Therefore, studies to develop IoT technologies are also increasing. Although the benefits of developing technology are enormous, it includes some difficulties. One of the most important challenges in IoT systems is resource allocation and management. Cloud, fog, or edge computing methods are used for storage and computing processes in IoT applications. Data perceived from resource-constrained devices reach these computing nodes. Resource allocation and management must be made in the cloud, fog, or edge nodes for computing and storage. The correct and complete resource allocation and management are very important for the performance of the system. Numerous methods are proposed for this. Artificial intelligence-based methods are one of them. This study examines IoT resource allocation and management.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Patel, K. K., & Patel, S. M. (2016). Internet of things-IOT: Definition, characteristics, architecture, enabling technologies, application & future challenges. International Journal of Engineering Science and Computing, 6(5).

    Google Scholar 

  2. Min, M., Xiao, L., Chen, Y., Cheng, P., Wu, D., & Zhuang, W. (2019). Learning-based computation offloading for IoT devices with energy harvesting. IEEE Transactions on Vehicular Technology, 68(2), 1930–1941.

    Article  Google Scholar 

  3. Bader, A., Ghazzai, H., Kadri, A., & Alouini, M. S. (2016). Front-end intelligence for large-scale application-oriented internet-of-things. IEEE Access, 4, 3257–3272.

    Article  Google Scholar 

  4. Varghese, B., Wang, N., Barbhuiya, S., Kilpatrick, P., & Nikolopoulos, D. S. (2016, November). Challenges and opportunities in edge computing. In 2016 IEEE International Conference on Smart Cloud (SmartCloud) (pp. 20–26). IEEE.

    Google Scholar 

  5. Mukherjee, M., Matam, R., Shu, L., Maglaras, L., Ferrag, M. A., Choudhury, N., & Kumar, V. (2017). Security and privacy in fog computing: Challenges. IEEE Access, 5, 19293–19304.

    Article  Google Scholar 

  6. Roman, R., Lopez, J., & Mambo, M. (2018). Mobile edge computing, fog et al.: A survey and analysis of security threats and challenges. Future Generation Computer Systems, 78, 680–698.

    Article  Google Scholar 

  7. Musaddiq, A., Zikria, Y. B., Hahm, O., Yu, H., Bashir, A. K., & Kim, S. W. (2018). A survey on resource management in IoT operating systems. IEEE Access, 6, 8459–8482.

    Article  Google Scholar 

  8. Choi, Y., & Lim, Y. (2016). Optimization approach for resource allocation on cloud computing for iot. International Journal of Distributed Sensor Networks, 12(3), 3479247.

    Article  Google Scholar 

  9. RamíÂrez, P. L. G., Taha, M., Lloret, J., & Tomás, J. (2019). An intelligent algorithm for resource sharing and self-management of wireless-IoT-gateway. IEEE Access, 8, 3159–3170.

    Google Scholar 

  10. Wang, H., Li, J., Tian, J., & Wang, K. (2019, November). WebIDE cloud server resource allocation with task pre-scheduling in IoT application development. In 2019 IEEE International Conference on Industrial Internet (ICII)

    Google Scholar 

  11. Sangaiah, A. K., Hosseinabadi, A. A. R., Shareh, M. B., Bozorgi Rad, S. Y., Zolfagharian, A., & Chilamkurti, N. (2020). IoT resource allocation and optimization based on heuristic algorithm. Sensors, 20(2), 539.

    Article  Google Scholar 

  12. Abedin, S. F., Alam, M. G. R., Kazmi, S. A., Tran, N. H., Niyato, D., & Hong, C. S. (2018). Resource allocation for ultra-reliable and enhanced mobile broadband IoT applications in fog network. IEEE Transactions on Communications, 67(1), 489–502.

    Article  Google Scholar 

  13. Gu, Y., Chang, Z., Pan, M., Song, L., & Han, Z. (2018). Joint radio and computational resource allocation in IoT fog computing. IEEE Transactions on Vehicular Technology, 67(8), 7475–7484.

    Article  Google Scholar 

  14. Nassar, A., & Yilmaz, Y. (2019). Reinforcement Learning for Adaptive Resource Allocation in Fog RAN for IoT With Heterogeneous Latency Requirements. IEEE Access, 7, 128014–128025.

    Article  Google Scholar 

  15. Banaie, F., Yaghmaee, M. H., Hosseini, A., & Tashtarian, F. (2020). Load-balancing algorithm for multiple gateways in Fog-based Internet of Things. IEEE Internet of Things Journal.

    Google Scholar 

  16. Huang, X., Cui, Y., Chen, Q., & Zhang, J. (2020). Joint task offloading and QoS-aware resource allocation in Fog-enabled Internet of Things networks. IEEE Internet of Things Journal.

    Google Scholar 

  17. Na, W., Jang, S., Lee, Y., Park, L., Dao, N. N., & Cho, S. (2018). Frequency resource allocation and interference management in mobile edge computing for an Internet of Things system. IEEE Internet of Things Journal, 6(3), 4910–4920.

    Article  Google Scholar 

  18. Deng, S., Xiang, Z., Zhao, P., Taheri, J., Gao, H., Yin, J., et al. (2020). Dynamical Resource Allocation in Edge for Trustable Internet-of-Things Systems: A Reinforcement Learning Method. IEEE Transactions on Industrial Informatics, 16(9), 6103–6113.

    Article  Google Scholar 

  19. Jiang, F., Wang, K., Dong, L., Pan, C., & Yang, K. (2020). Stacked auto encoder based deep reinforcement learning for online resource scheduling in large-scale MEC networks. IEEE Internet of Things Journal.

    Google Scholar 

  20. Xu, S., Liu, Q., Gong, B., Qi, F., Guo, S., Qiu, X., et al. (2020). RJCC: Reinforcement-Learning-Based Joint Communicational-and-Computational Resource Allocation Mechanism for Smart City IoT. IEEE Internet of Things Journal, 7(9), 8059–8076.

    Article  Google Scholar 

  21. Liu, X., Yu, J., Wang, J., & Gao, Y. (2020). Resource Allocation With Edge Computing in IoT Networks via Machine Learning. IEEE Internet of Things Journal, 7(4), 3415–3426.

    Article  Google Scholar 

  22. Tang, L., & Hu, H. (2020). Computation Offloading and Resource Allocation for the Internet of Things in Energy-Constrained MEC-Enabled HetNets. IEEE Access, 8, 47509–47521.

    Article  Google Scholar 

  23. Wang, Q., Shao, S., Guo, S., Qiu, X., & Wang, Z. (2020). Task Allocation Mechanism of Power Internet of Things Based on Cooperative Edge Computing. IEEE Access, 8, 158488–158501.

    Article  Google Scholar 

  24. Xiong, X., Zheng, K., Lei, L., & Hou, L. (2020). Resource Allocation Based on Deep Reinforcement Learning in IoT Edge Computing. IEEE Journal on Selected Areas in Communications, 38(6), 1133–1146.

    Article  Google Scholar 

  25. Zhang, Q., Gui, L., Hou, F., Chen, J., Zhu, S., & Tian, F. (2020). Dynamic Task Offloading and Resource Allocation for Mobile-Edge Computing in Dense Cloud RAN. IEEE Internet of Things Journal, 7(4), 3282–3299.

    Article  Google Scholar 

  26. Khan, L. U., Alsenwi, M., Yaqoob, I., Imran, M., Han, Z., & Hong, C. S. (2020). Resource Optimized Federated Learning-Enabled Cognitive Internet of Things for Smart Industries. IEEE Access, 8, 168854–168864.

    Article  Google Scholar 

  27. Lee, J., Kim, D. J., & Niyato, D. (2020). Market analysis of distributed learning resource management for Internet of Things: A game theoretic approach. IEEE Internet of Things Journal.

    Google Scholar 

  28. Pham, T. M. (2020). Optimization of Resource Management for NFV-Enabled IoT Systems in Edge Cloud Computing. IEEE Access, 8, 178217–178229.

    Article  Google Scholar 

  29. He, X., Wang, K., Huang, H., Miyazaki, T., Wang, Y., & Guo, S. (2018). Green resource allocation based on deep reinforcement learning in content-centric IoT. IEEE Transactions on Emerging Topics in Computing.

    Google Scholar 

  30. Zhang, D., Qiao, Y., She, L., Shen, R., Ren, J., & Zhang, Y. (2018). Two time-scale resource management for green Internet of Things networks. IEEE Internet of Things Journal, 6(1), 545–556.

    Article  Google Scholar 

  31. Liu, M., Song, T., & Gui, G. (2018). Deep cognitive perspective: Resource allocation for NOMA-based heterogeneous IoT with imperfect SIC. IEEE Internet of Things Journal, 6(2), 2885–2894.

    Article  Google Scholar 

  32. Malik, H., Pervaiz, H., Alam, M. M., Le Moullec, Y., Kuusik, A., & Imran, M. A. (2018). Radio resource management scheme in NB-IoT systems. IEEE Access, 6, 15051–15064.

    Article  Google Scholar 

  33. Su, J., Xu, H., Xin, N., Cao, G., & Zhou, X. (2018). Resource allocation in wireless powered iot system: a mean field stackelberg game-based approach. Sensors, 18(10), 3173.

    Article  Google Scholar 

  34. Dou, Z., Si, G., Lin, Y., & Wang, M. (2019). An adaptive resource allocation model with anti-jamming in IoT network. IEEE Access, 7, 93250–93258.

    Article  Google Scholar 

  35. Hussain, F., Hussain, R., Anpalagan, A., & Benslimane, A. (2020). A New Block-Based Reinforcement Learning Approach for Distributed Resource Allocation in Clustered IoT Networks. IEEE Transactions on Vehicular Technology, 69(3), 2891–2904.

    Article  Google Scholar 

  36. Liu, X., Qin, Z., Gao, Y., & McCann, J. A. (2019). Resource allocation in wireless powered IoT networks. IEEE Internet of Things Journal, 6(3), 4935–4945.

    Article  Google Scholar 

  37. He, Y., Zhang, S., Tang, L., & Ren, Y. (2020). Large Scale Resource Allocation for the Internet of Things Network Based on ADMM. IEEE Access, 8, 57192–57203.

    Article  Google Scholar 

  38. Yang, H., Zhong, W. D., Chen, C., Alphones, A., & Xie, X. (2020). Deep reinforcement learning based energy-efficient resource management for social and cognitive Internet of Things. IEEE Internet of Things Journal.

    Google Scholar 

  39. Librino, F., & Santi, P. (2020). Resource Allocation and Sharing in URLLC for IoT Applications Using Shareability Graphs. IEEE Internet of Things Journal, 7(10), 10511–10526.

    Article  Google Scholar 

  40. Chen, D., Yang, C., Gong, P., Chang, L., Shao, J., Ni, Q., et al. (2020). Resource Cube: Multi-Virtual Resource Management for Integrated Satellite-Terrestrial Industrial IoT Networks. IEEE Transactions on Vehicular Technology, 69(10), 11963–11974.

    Article  Google Scholar 

  41. Akleylek, S., Soysaldi, M., Boubiche, D. E., & Toral-Cruz, H. (2019). A novel method for polar form of any degree of multivariate polynomials with applications in IoT. Sensors, 19(4), 903.

    Google Scholar 

  42. Akleylek, S., & Seyhan, K. (2020). A probably secure bi-GISIS based modified AKE scheme with reusable keys. IEEE Access, 8, 26210–26222.

    Article  Google Scholar 

  43. Akleylek, S. & Karakaya, A. (2020). Data security in fog computing and applications. In S. Sagiroglu, & S. Akleylek (Eds.), Cyber security and defense: Biometric and cryptographic applications. Turkey: Nobel Academic Publishing. ISBN:978-625-439-024-1.

    Google Scholar 

  44. Karakaya, A., & Akleylek, S. (2021). A novel IoT-based health and tactical analysis model with fog computing. PeerJ Computer Science, 7. https://doi.org/10.7717/peerj-cs.342

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Aykut Karakaya .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Karakaya, A., Akleylek, S. (2022). A Review of Resource Allocation and Management Methods in IoT. In: Kumar, P., Obaid, A.J., Cengiz, K., Khanna, A., Balas, V.E. (eds) A Fusion of Artificial Intelligence and Internet of Things for Emerging Cyber Systems. Intelligent Systems Reference Library, vol 210. Springer, Cham. https://doi.org/10.1007/978-3-030-76653-5_22

Download citation

Publish with us

Policies and ethics